Remote snappable linking

ABSTRACT

In some examples, a cluster comprises peer nodes and a distributed data store implemented across the peer nodes, a method of remote linking of data objects for data transfer between a first node cluster and a second node cluster among the peer nodes, the method comprising: creating a data object group including multiple remote data objects, wherein a plurality of remote data objects in the data object group represent a same first virtual machine and are registrable on at least the first and second node clusters of the peer DMS nodes; creating or identifying remote links to a plurality of the remote data objects in the data object group; designating a member of the data object group as an active member of the group; and assigning a task to the active member to be completed using remote links.

CROSS REFERENCE

The present application for patent is a continuation of U.S. patent application Ser. No. 17/567,946 by AGRAWAL et al., entitled “REMOTE SNAPPABLE LINKING,” filed Jan. 4, 2022, which is a continuation of U.S. patent application Ser. No. 17/150,130 by AGRAWAL et al., entitled “REMOTE SNAPPABLE LINKING,” filed Jan. 15, 2021, each of which is assigned to the assignee hereof, and each of which is expressly incorporated by reference herein.

FIELD

The present disclosure relates generally to computer architecture software for a data management platform and, in some more particular aspects, to systems and methods for remote data object linking.

BACKGROUND

The volume and complexity of data that is collected, analyzed, and stored is increasing rapidly over time. The computer infrastructure used to handle this data is also becoming more complex, with more processing power and more portability. As a result, data management and storage is becoming increasingly important. Significant issues of these processes include access to reliable data backup and storage and fast data recovery in cases of failure. Other aspects include data portability across locations and platforms.

In an example background scenario, a computer system administrator may run their production workload in a datacenter (for example, a primary datacenter). The primary data set may be protected by a primary cluster of recovery nodes. In the case of a disaster in the primary datacenter, the administrator may failover the workload to a secondary datacenter, which may be protected by a secondary cluster of recovery nodes. In the failover workflow, a number of new virtual machines (VMs) may be created in the secondary datacenter.

In some situations, this can cause problems. For example, a first VM backup after the failover may be full for each of the failed-over VMs as there is no snapshot history for the failed-over VMs in the new cluster. Secondly, if the administrator tries to perform failback to a pre-failover data center and cluster, the first replication to the pre-failover cluster may be full. Further, both the pre-failover VM and post failover VM will each be shown as a different entity in a management user interface even if these entities are processing the same logical workload. The present disclosure seeks to address these issues.

The background description provided herein is to generally present the context of the disclosure. It should be noted that the information described in this section is presented to provide the skilled artisan some context for the following disclosed subject matter and should not be considered as admitted prior art. More specifically, work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

SUMMARY

In some examples, a system is provided for remote data object linking. In this disclosure, the term “data object” refers to a data object or file that is capable of being copied or backed up, or of which a snapshot can be taken. Some examples herein include a capability referred to as “remote data object linking,” in which examples are able to assemble multiple different remote data objects, and links to the remote data objects, into a single group. The term “remote” is intended to mean that the different data objects can be registered on different node clusters, and not merely the same, or a local, cluster.

Some examples create a single entity including a group of members for managing and processing multiple remote data objects. In some example groups, only one member of the entity is designated to act as an overall active member to schedule various jobs such as backup, replication, and so forth. In some examples, an system administrator is unable to schedule any jobs which generate data (such as backup and replication jobs) for members in the group other than the overall active member.

In some examples a data management and storage (DMS) cluster comprises peer DMS nodes and a distributed data store implemented across the peer DMS nodes, wherein the peer DMS nodes are configured to collectively implement operations in a method of remote linking of data objects for data transfer between a first node cluster and a second node cluster among the peer DMS nodes, the operations comprising: identifying the first node cluster; identifying the second node cluster; creating a data object group including multiple remote data objects, each remote data object representing a same first virtual machine, each remote data object registrable on at least the first and second node clusters of the peer DMS nodes; creating or identifying remote links to each of the remote data objects in the data object group; designating a single member of the data object group as an overall active member of the group; assigning, to the active remember only, a backup or replication job of the virtual machine; and based on the assigned job, backing up the virtual machine using the remote data object links.

In some examples, the backup or replication job is associated with a failover operation or an instant recovery of the first virtual machine; and wherein the operations further comprise: creating, in the second cluster, a second data object group comprising a first remote link to a first remote data object associated with the first virtual machine, and a second remote link to a second remote data object relating to a second virtual machine; and designating the second virtual machine as the overall active member.

In some examples, the operations further comprise based on a backup or replication request relating to the second virtual machine, storing a backup of the second virtual machine incrementally on top of replicated snapshots of the first virtual machine.

In some examples, the first node cluster is a source cluster; and the backup or replication job is associated with a source cluster refresh (SCR); and the operations further comprise: identifying the second data object group in the second cluster created during the failover operation or instant recovery of the first virtual machine; recognizing a same data object group in the first node cluster; and based on the recognition, performing the SCR incrementally.

In some examples, the operations further comprise resolving a discrepancy in the designation of the overall active member in response to an identification of a new member of the data object group.

In some examples, the data object group further comprises at least one local data object; and the operations further comprise linking data objects together based on an elevated level of trust assigned to the at least one local data object.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation in the views of the accompanying drawing:

FIG. 1 depicts one embodiment of a networked computing environment in which the disclosed technology may be practiced, according to an example embodiment.

FIG. 2 depicts one embodiment of the server of FIG. 1 , according to an example embodiment.

FIG. 3 depicts one embodiment of the storage appliance of FIG. 1 , according to an example embodiment.

FIG. 4 shows an example cluster of a distributed decentralized database, according to some example embodiments.

FIGS. 5-9B illustrate aspects of the subject matter, in accordance with various embodiments.

FIG. 10 depicts a block flow chart indicating example operations in a method, according to example embodiments.

FIG. 11 depicts a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments.

FIG. 12 depicts a block diagram illustrating an architecture of software, according to an example embodiment

FIG. 13 illustrates a diagrammatic representation of a machine 1000 in the form of a computer system within which a set of instructions may be executed for causing a machine to perform any one or more of the methodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the present disclosure. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present inventive subject matter may be practiced without these specific details. The use of the term “instant” in this disclosure means instant or “near instant” in the context of a computing platform as will be appreciated by a person of ordinary skill in the art.

A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to the software and data as described below and in the drawings that form a part of this document: Copyright Rubrik, Inc., 2020, All Rights Reserved.

It will be appreciated that some of the examples disclosed herein are described in the context of virtual machines that are backed up by using base and incremental snapshots, for example. This should not necessarily be regarded as limiting of the disclosures. The disclosures, systems, and methods described herein apply not only to virtual machines of all types that run a file system (for example), but also to Network Attached Storage (NAS) devices, physical machines (for example Linux servers), and databases.

FIG. 1 depicts one embodiment of a networked computing environment 100 in which the disclosed technology may be practiced. As depicted, the networked computing environment 100 includes a data center 106, a storage appliance 102, and a computing device 108 in communication with each other via one or more networks 128. The networked computing environment 100 may also include a plurality of computing devices interconnected through one or more networks 128. The one or more networks 128 may allow computing devices and/or storage devices to connect to and communicate with other computing devices and/or other storage devices. In some cases, the networked computing environment 100 may include other computing devices and/or other storage devices not shown. The other computing devices may include, for example, a mobile computing device, a non-mobile computing device, a server, a workstation, a laptop computer, a tablet computer, a desktop computer, or an information processing system. The other storage devices may include, for example, a storage area network storage device, a networked-attached storage device, a hard disk drive, a solid-state drive, or a data storage system.

The data center 106 may include one or more servers, such as server 200, in communication with one or more storage devices, such as storage device 104. The one or more servers may also be in communication with one or more storage appliances, such as storage appliance 102. The server 200, storage device 104, and storage appliance 300 may be in communication with each other via a networking fabric connecting servers and data storage units within the data center 106 to each other. The storage appliance 300 may include a data management system for backing up virtual machines and/or files within a virtualized infrastructure. The server 200 may be used to create and manage one or more virtual machines associated with a virtualized infrastructure.

The one or more virtual machines may run various applications, such as a database application or a web server. The storage device 104 may include one or more hardware storage devices for storing data, such as a hard disk drive (HDD), a magnetic tape drive, a solid-state drive (SSD), a storage area network (SAN) storage device, or a NAS device. In some cases, a data center, such as data center 106, may include thousands of servers and/or data storage devices in communication with each other. The one or more data storage devices 104 may comprise a tiered data storage infrastructure (or a portion of a tiered data storage infrastructure). The tiered data storage infrastructure may allow for the movement of data across different tiers of a data storage infrastructure between higher-cost, higher-performance storage devices (e.g., solid-state drives and hard disk drives) and relatively lower-cost, lower-performance storage devices (e.g., magnetic tape drives).

The one or more networks 128 may include a secure network such as an enterprise private network, an unsecure network such as a wireless open network, a local area network (LAN), a wide area network (WAN), and the Internet. The one or more networks 128 may include a cellular network, a mobile network, a wireless network, or a wired network. Each network of the one or more networks 128 may include hubs, bridges, routers, switches, and wired transmission media such as a direct-wired connection. The one or more networks 128 may include an extranet or other private network for securely sharing information or providing controlled access to applications or files.

A server, such as server 200, may allow a client to download information or files (e.g., executable, text, application, audio, image, or video files) from the server 200 or to perform a search query related to particular information stored on the server 200. In some cases, a server may act as an application server or a file server. In general, server 200 may refer to a hardware device that acts as the host in a client-server relationship or a software process that shares a resource with or performs work for one or more clients.

One embodiment of server 200 includes a network interface 110, processor 112, memory 114, disk 116, and virtualization manager 118 all in communication with each other. Network interface 110 allows server 200 to connect to one or more networks 128. Network interface 110 may include a wireless network interface and/or a wired network interface. Processor 112 allows server 200 to execute computer-readable instructions stored in memory 114 in order to perform processes described herein. Processor 112 may include one or more processing units, such as one or more CPUs and/or one or more GPUs. Memory 114 may comprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM, EEPROM, Flash, etc.). Disk 116 may include a hard disk drive and/or a solid-state drive. Memory 114 and disk 116 may comprise hardware storage devices.

The virtualization manager 118 may manage a virtualized infrastructure and perform management operations associated with the virtualized infrastructure. The virtualization manager 118 may manage the provisioning of virtual machines running within the virtualized infrastructure and provide an interface to computing devices interacting with the virtualized infrastructure. In one example, the virtualization manager 118 may set a virtual machine having a virtual disk into a frozen state in response to a snapshot request made via an application programming interface (API) by a storage appliance, such as storage appliance 300. Setting the virtual machine into a frozen state may allow a point in time snapshot of the virtual machine to be stored or transferred. In one example, updates made to a virtual machine that has been set into a frozen state may be written to a separate file (e.g., an update file) while the virtual disk may be set into a read-only state to prevent modifications to the virtual disk file while the virtual machine is in the frozen state.

The virtualization manager 118 may then transfer data associated with the virtual machine (e.g., an image of the virtual machine or a portion of the image of the virtual disk file associated with the state of the virtual disk at the point in time it is frozen) to a storage appliance (for example, a storage appliance 102 or storage appliance 300 of FIG. 1 , described further below) in response to a request made by the storage appliance. After the data associated with the point in time snapshot of the virtual machine has been transferred to the storage appliance 300 (for example), the virtual machine may be released from the frozen state (i.e., unfrozen) and the updates made to the virtual machine and stored in the separate file may be merged into the virtual disk file. The virtualization manager 118 may perform various virtual machine-related tasks, such as cloning virtual machines, creating new virtual machines, monitoring the state of virtual machines, moving virtual machines between physical hosts for load balancing purposes, and facilitating backups of virtual machines.

One embodiment of a storage appliance 300 (or storage appliance 102) includes a network interface 120, processor 122, memory 124, and disk 126 all in communication with each other. Network interface 120 allows storage appliance 300 to connect to one or more networks 128. Network interface 120 may include a wireless network interface and/or a wired network interface. Processor 122 allows storage appliance 300 to execute computer readable instructions stored in memory 124 in order to perform processes described herein. Processor 122 may include one or more processing units, such as one or more CPUs and/or one or more GPUs. Memory 124 may comprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM, EEPROM, NOR Flash, NAND Flash, etc.). Disk 126 may include a hard disk drive and/or a solid-state drive. Memory 124 and disk 126 may comprise hardware storage devices.

In one embodiment, the storage appliance 300 may include four machines. Each of the four machines may include a multi-core CPU, 64 GB of RAM, a 400 GB SSD, three 4 TB HDDs, and a network interface controller. In this case, the four machines may be in communication with the one or more networks 128 via the four network interface controllers. The four machines may comprise four nodes of a server cluster. The server cluster may comprise a set of physical machines that are connected together via a network. The server cluster may be used for storing data associated with a plurality of virtual machines, such as backup data associated with different point-in-time versions of the virtual machines.

The networked computing environment 100 may provide a cloud computing environment for one or more computing devices. Cloud computing may refer to Internet-based computing, wherein shared resources, software, and/or information may be provided to one or more computing devices on-demand via the Internet. The networked computing environment 100 may comprise a cloud computing environment providing Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) services. SaaS may refer to a software distribution model in which applications are hosted by a service provider and made available to end users over the Internet. In one embodiment, the networked computing environment 100 may include a virtualized infrastructure that provides software, data processing, and/or data storage services to end users accessing the services via the networked computing environment 100. In one example, networked computing environment 100 may provide cloud-based work productivity or business-related applications to a computing device, such as computing device 108. The storage appliance 102 may comprise a cloud-based data management system for backing up virtual machines and/or files within a virtualized infrastructure, such as virtual machines running on server 200/or files stored on server 200.

In some cases, networked computing environment 100 may provide remote access to secure applications and files stored within data center 106 from a remote computing device, such as computing device 108. The data center 106 may use an access control application to manage remote access to protected resources, such as protected applications, databases, or files located within the data center 106. To facilitate remote access to secure applications and files, a secure network connection may be established using a virtual private network (VPN). A VPN connection may allow a remote computing device, such as computing device 108, to securely access data from a private network (e.g., from a company file server or mail server) using an unsecure public network or the Internet. The VPN connection may require client-side software (e.g., running on the remote computing device) to establish and maintain the VPN connection. The VPN client software may provide data encryption and encapsulation prior to the transmission of secure private network traffic through the Internet.

In some embodiments, the storage appliance 300 may manage the extraction and storage of virtual machine snapshots associated with different point in time versions of one or more virtual machines running within the data center 106. A snapshot of a virtual machine may correspond with a state of the virtual machine at a particular point-in-time. In response to a restore command from the storage device 104, the storage appliance 300 may restore a point-in-time version of a virtual machine or restore point-in-time versions of one or more files located on the virtual machine and transmit the restored data to the server 200. In response to a mount command from the server 200, the storage appliance 300 may allow a point-in-time version of a virtual machine to be mounted and allow the server 200 to read and/or modify data associated with the point-in-time version of the virtual machine. To improve storage density, the storage appliance 300 may deduplicate and compress data associated with different versions of a virtual machine and/or deduplicate and compress data associated with different virtual machines. To improve system performance, the storage appliance 300 may first store virtual machine snapshots received from a virtualized environment in a cache, such as a flash-based cache. The cache may also store popular data or frequently accessed data (e.g., based on a history of virtual machine restorations, incremental files associated with commonly restored virtual machine versions) and current day incremental files or incremental files corresponding with snapshots captured within the past 24 hours.

An incremental file may comprise a forward incremental file or a reverse incremental file. A forward incremental file may include a set of data representing changes that have occurred since an earlier point-in-time snapshot of a virtual machine. To generate a snapshot of the virtual machine corresponding with a forward incremental file, the forward incremental file may be combined with an earlier point in time snapshot of the virtual machine (e.g., the forward incremental file may be combined with the last full image of the virtual machine that was captured before the forward incremental file was captured and any other forward incremental files that were captured subsequent to the last full image and prior to the forward incremental file). A reverse incremental file may include a set of data representing changes from a later point-in-time snapshot of a virtual machine. To generate a snapshot of the virtual machine corresponding with a reverse incremental file, the reverse incremental file may be combined with a later point-in-time snapshot of the virtual machine (e.g., the reverse incremental file may be combined with the most recent snapshot of the virtual machine and any other reverse incremental files that were captured prior to the most recent snapshot and subsequent to the reverse incremental file).

The storage appliance 300 may provide a user interface (e.g., a web-based interface or a graphical user interface) that displays virtual machine backup information such as identifications of the virtual machines protected and the historical versions or time machine views for each of the virtual machines protected. A time machine view of a virtual machine may include snapshots of the virtual machine over a plurality of points in time. Each snapshot may comprise the state of the virtual machine at a particular point in time. Each snapshot may correspond with a different version of the virtual machine (e.g., Version 1 of a virtual machine may correspond with the state of the virtual machine at a first point in time and Version 2 of the virtual machine may correspond with the state of the virtual machine at a second point in time subsequent to the first point in time).

The user interface may enable an end user of the storage appliance 300 (e.g., a system administrator or a virtualization administrator) to select a particular version of a virtual machine to be restored or mounted. When a particular version of a virtual machine has been mounted, the particular version may be accessed by a client (e.g., a virtual machine, a physical machine, or a computing device) as if the particular version was local to the client. A mounted version of a virtual machine may correspond with a mount point directory (e.g., /snapshots/VM5Nersion23). In one example, the storage appliance 300 may run an NFS server and make the particular version (or a copy of the particular version) of the virtual machine accessible for reading and/or writing. The end user of the storage appliance 300 may then select the particular version to be mounted and run an application (e.g., a data analytics application) using the mounted version of the virtual machine. In another example, the particular version may be mounted as an iSCSI target.

FIG. 2 depicts one embodiment of server 200 of FIG. 1 . The server 200 may comprise one server out of a plurality of servers that are networked together within a data center (e.g., data center 106). In one example, the plurality of servers may be positioned within one or more server racks within the data center. As depicted, the server 200 includes hardware-level components and software-level components. The hardware-level components include one or more processors 202, one or more memories 204, and one or more disks 206. The software-level components include a hypervisor 208, a virtualized infrastructure manager 222, and one or more virtual machines, such as virtual machine 220. The hypervisor 208 may comprise a native hypervisor or a hosted hypervisor. The hypervisor 208 may provide a virtual operating platform for running one or more virtual machines, such as virtual machine 220. Virtual machine 220 includes a plurality of virtual hardware devices including a virtual processor 210, a virtual memory 212, and a virtual disk 214. The virtual disk 214 may comprise a file stored within the one or more disks 206. In one example, a virtual machine 220 may include a plurality of virtual disks 214, with each virtual disk of the plurality of virtual disks 214 associated with a different file stored on the one or more disks 206. Virtual machine 220 may include a guest operating system 216 that runs one or more applications, such as application 218.

The virtualized infrastructure manager 222, which may correspond with the virtualization manager 118 in FIG. 1 , may run on a virtual machine or natively on the server 200. The virtual machine may, for example, be or include the virtual machine 220 or a virtual machine separate from the server 200. Other arrangements are possible. The virtualized infrastructure manager 222 may provide a centralized platform for managing a virtualized infrastructure that includes a plurality of virtual machines. The virtualized infrastructure manager 222 may manage the provisioning of virtual machines running within the virtualized infrastructure and provide an interface to computing devices interacting with the virtualized infrastructure. The virtualized infrastructure manager 222 may perform various virtualized infrastructure related tasks, such as cloning virtual machines, creating new virtual machines, monitoring the state of virtual machines, and facilitating backups of virtual machines.

In one embodiment, the server 200 may use the virtualized infrastructure manager 222 to facilitate backups for a plurality of virtual machines (e.g., eight different virtual machines) running on the server 200. Each virtual machine running on the server 200 may run its own guest operating system and its own set of applications. Each virtual machine running on the server 200 may store its own set of files using one or more virtual disks associated with the virtual machine (e.g., each virtual machine may include two virtual disks that are used for storing data associated with the virtual machine).

In one embodiment, a data management application running on a storage appliance, such as storage appliance 102 in FIG. 1 or storage appliance 300 in FIG. 1 , may request a snapshot of a virtual machine running on server 200. The snapshot of the virtual machine may be stored as one or more files, with each file associated with a virtual disk of the virtual machine. A snapshot of a virtual machine may correspond with a state of the virtual machine at a particular point in time. The particular point in time may be associated with a time stamp. In one example, a first snapshot of a virtual machine may correspond with a first state of the virtual machine (including the state of applications and files stored on the virtual machine) at a first point in time, and a second snapshot of the virtual machine may correspond with a second state of the virtual machine at a second point in time subsequent to the first point in time.

In response to a request for a snapshot of a virtual machine at a particular point in time, the virtualized infrastructure manager 222 may set the virtual machine into a frozen state or store a copy of the virtual machine at the particular point in time. The virtualized infrastructure manager 222 may then transfer data associated with the virtual machine (e.g., an image of the virtual machine or a portion of the image of the virtual machine) to the storage appliance 300 or storage appliance 102. The data associated with the virtual machine may include a set of files including a virtual disk file storing contents of a virtual disk of the virtual machine at the particular point in time and a virtual machine configuration file storing configuration settings for the virtual machine at the particular point in time. The contents of the virtual disk file may include the operating system used by the virtual machine, local applications stored on the virtual disk, and user files (e.g., images and word processing documents). In some cases, the virtualized infrastructure manager 222 may transfer a full image of the virtual machine to the storage appliance 102 or storage appliance 300 of FIG. 1 or a plurality of data blocks corresponding with the full image (e.g., to enable a full image-level backup of the virtual machine to be stored on the storage appliance). In other cases, the virtualized infrastructure manager 222 may transfer a portion of an image of the virtual machine associated with data that has changed since an earlier point in time prior to the particular point in time or since a last snapshot of the virtual machine was taken. In one example, the virtualized infrastructure manager 222 may transfer only data associated with virtual blocks stored on a virtual disk of the virtual machine that have changed since the last snapshot of the virtual machine was taken. In one embodiment, the data management application may specify a first point in time and a second point in time and the virtualized infrastructure manager 222 may output one or more virtual data blocks associated with the virtual machine that have been modified between the first point in time and the second point in time.

In some embodiments, the server 200 or the hypervisor 208 may communicate with a storage appliance, such as storage appliance 102 in FIG. 1 or storage appliance 300 in FIG. 1 , using a distributed file system protocol such as NFS Version 3, or Server Message Block (SMB) protocol. The distributed file system protocol may allow the server 200 or the hypervisor 208 to access, read, write, or modify files stored on the storage appliance as if the files were locally stored on the server 200. The distributed file system protocol may allow the server 200 or the hypervisor 208 to mount a directory or a portion of a file system located within the storage appliance.

FIG. 3 depicts one embodiment of storage appliance 300 in FIG. 1 . The storage appliance may include a plurality of physical machines that may be grouped together and presented as a single computing system. Each physical machine of the plurality of physical machines may comprise a node in a cluster (e.g., a failover cluster). In one example, the storage appliance may be positioned within a server rack within a data center. As depicted, the storage appliance 300 includes hardware-level components and software-level components. The hardware-level components include one or more physical machines, such as physical machine 314 and physical machine 324. The physical machine 314 includes a network interface 316, processor 318, memory 320, and disk 322 all in communication with each other. Processor 318 allows physical machine 314 to execute computer readable instructions stored in memory 320 to perform processes described herein. Disk 322 may include a hard disk drive and/or a solid-state drive. The physical machine 324 includes a network interface 326, processor 328, memory 330, and disk 332 all in communication with each other. Processor 328 allows physical machine 324 to execute computer readable instructions stored in memory 330 to perform processes described herein. Disk 332 may include a hard disk drive and/or a solid-state drive. In some cases, disk 332 may include a flash-based SSD or a hybrid HDD/SSD drive. In one embodiment, the storage appliance 300 may include a plurality of physical machines arranged in a cluster (e.g., eight machines in a cluster). Each of the plurality of physical machines may include a plurality of multi-core CPUs, 108 GB of RAM, a 500 GB SSD, four 4 TB HDDs, and a network interface controller.

In some embodiments, the plurality of physical machines may be used to implement a cluster-based network fileserver. The cluster-based network file server may neither require nor use a front-end load balancer. One issue with using a front-end load balancer to host the IP address for the cluster-based network file server and to forward requests to the nodes of the cluster-based network file server is that the front-end load balancer comprises a single point of failure for the cluster-based network file server. In some cases, the file system protocol used by a server, such as server 200 in FIG. 1 , or a hypervisor, such as hypervisor 208 in FIG. 2 , to communicate with the storage appliance 300 may not provide a failover mechanism (e.g., NFS Version 3). In the case that no failover mechanism is provided on the client side, the hypervisor may not be able to connect to a new node within a cluster in the event that the node connected to the hypervisor fails.

In some embodiments, each node in a cluster may be connected to each other via a network and may be associated with one or more IP addresses (e.g., two different IP addresses may be assigned to each node). In one example, each node in the cluster may be assigned a permanent IP address and a floating IP address and may be accessed using either the permanent IP address or the floating IP address. In this case, a hypervisor, such as hypervisor 208 in FIG. 2 , may be configured with a first floating IP address associated with a first node in the cluster. The hypervisor may connect to the cluster using the first floating IP address. In one example, the hypervisor may communicate with the cluster using the NFS Version 3 protocol. Each node in the cluster may run a Virtual Router Redundancy Protocol (VRRP) daemon. A daemon may comprise a background process. Each VRRP daemon may include a list of all floating IP addresses available within the cluster. In the event that the first node associated with the first floating IP address fails, one of the VRRP daemons may automatically assume or pick up the first floating IP address if no other VRRP daemon has already assumed the first floating IP address. Therefore, if the first node in the cluster fails or otherwise goes down, then one of the remaining VRRP daemons running on the other nodes in the cluster may assume the first floating IP address that is used by the hypervisor for communicating with the cluster.

In order to determine which of the other nodes in the cluster will assume the first floating IP address, a VRRP priority may be established. In one example, given a number (N) of nodes in a cluster from node(0) to node(N−1), for a floating IP address (i), the VRRP priority of nodeG) may be G−i) modulo N. In another example, given a number (N) of nodes in a cluster from node(0) to node(N−1), for a floating IP address (i), the VRRP priority of nodeG) may be (i−j) modulo N. In these cases, nodeG) will assume floating IP address (i) only if its VRRP priority is higher than that of any other node in the cluster that is alive and announcing itself on the network. Thus, if a node fails, then there may be a clear priority ordering for determining which other node in the cluster will take over the failed node's floating IP address.

In some cases, a cluster may include a plurality of nodes and each node of the plurality of nodes may be assigned a different floating IP address. In this case, a first hypervisor may be configured with a first floating IP address associated with a first node in the cluster, a second hypervisor may be configured with a second floating IP address associated with a second node in the cluster, and a third hypervisor may be configured with a third floating IP address associated with a third node in the cluster.

As depicted in FIG. 3 , the software-level components of the storage appliance 300 may include data management system 302, a virtualization interface 304, a distributed job scheduler 308, a distributed metadata store 310, a distributed file system 312, and one or more virtual machine search indexes, such as virtual machine search index 306. In one embodiment, the software-level components of the storage appliance 300 may be run using a dedicated hardware-based appliance. In another embodiment, the software-level components of the storage appliance 300 may be run from the cloud (e.g., the software-level components may be installed on a cloud service provider).

In some cases, the data storage across a plurality of nodes in a cluster (e.g., the data storage available from the one or more physical machine (e.g., physical machine 314 and physical machine 324)) may be aggregated and made available over a single file system namespace (e.g., /snapshots/). A directory for each virtual machine protected using the storage appliance 300 may be created (e.g., the directory for Virtual Machine A may be /snapshots/VM_A). Snapshots and other data associated with a virtual machine may reside within the directory for the virtual machine. In one example, snapshots of a virtual machine may be stored in subdirectories of the directory (e.g., a first snapshot of Virtual Machine A may reside in /snapshots/VM_A/s1/ and a second snapshot of Virtual Machine A may reside in /snapshots/VM_A/s2/).

The distributed file system 312 may present itself as a single file system, in which. as new physical machines or nodes are added to the storage appliance 300, the cluster may automatically discover the additional nodes and automatically increase the available capacity of the file system for storing files and other data. Each file stored in the distributed file system 312 may be partitioned into one or more chunks or shards. Each of the one or more chunks may be stored within the distributed file system 312 as a separate file. The files stored within the distributed file system 312 may be replicated or mirrored over a plurality of physical machines, thereby creating a load-balanced and fault tolerant distributed file system. In one example, storage appliance 300 may include ten physical machines arranged as a failover cluster and a first file corresponding with a snapshot of a virtual machine (e.g., /snapshots/VM_A/s1/s1.full) may be replicated and stored on three of the ten machines.

The distributed metadata store 310 may include a distributed database management system that provides high availability without a single point of failure. In one embodiment, the distributed metadata store 310 may comprise a database, such as a distributed document-oriented database. The distributed metadata store 310 may be used as a distributed key value storage system. In one example, the distributed metadata store 310 may comprise a distributed NoSQL key value store database. In some cases, the distributed metadata store 310 may include a partitioned row store, in which rows are organized into tables or other collections of related data held within a structured format within the key value store database. A table (or a set of tables) may be used to store metadata information associated with one or more files stored within the distributed file system 312. The metadata information may include the name of a file, a size of the file, file permissions associated with the file, when the file was last modified, and file mapping information associated with an identification of the location of the file stored within a cluster of physical machines. In one embodiment, a new file corresponding with a snapshot of a virtual machine may be stored within the distributed file system 312 and metadata associated with the new file may be stored within the distributed metadata store 310. The distributed metadata store 310 may also be used to store a backup schedule for the virtual machine and a list of snapshots for the virtual machine that are stored using the storage appliance 300.

In some cases, the distributed metadata store 310 may be used to manage one or more versions of a virtual machine. Each version of the virtual machine may correspond with a full image snapshot of the virtual machine stored within the distributed file system 312 or an incremental snapshot of the virtual machine (e.g., a forward incremental or reverse incremental) stored within the distributed file system 312. In one embodiment, the one or more versions of the virtual machine may correspond with a plurality of files. The plurality of files may include a single full image snapshot of the virtual machine and one or more incremental aspects derived from the single full image snapshot. The single full image snapshot of the virtual machine may be stored using a first storage device of a first type (e.g., a HDD) and the one or more incremental aspects derived from the single full image snapshot may be stored using a second storage device of a second type (e.g., an SSD). In this case, only a single full image needs to be stored, and each version of the virtual machine may be generated from the single full image or the single full image combined with a subset of the one or more incremental aspects. Furthermore, each version of the virtual machine may be generated by performing a sequential read from the first storage device (e.g., reading a single file from a HDD) to acquire the full image and, in parallel, performing one or more reads from the second storage device (e.g., performing fast random reads from an SSD) to acquire the one or more incremental aspects.

The distributed job scheduler 308 may be used for scheduling backup jobs that acquire and store virtual machine snapshots for one or more virtual machines over time. The distributed job scheduler 308 may follow a backup schedule to back up an entire image of a virtual machine at a particular point in time or one or more virtual disks associated with the virtual machine at the particular point in time. In one example, the backup schedule may specify that the virtual machine be backed up at a snapshot capture frequency, such as every two hours or every 24 hours. Each backup job may be associated with one or more tasks to be performed in a sequence. Each of the one or more tasks associated with a job may be run on a particular node within a cluster. In some cases, the distributed job scheduler 308 may schedule a specific job to be run on a particular node based on data stored on the particular node. For example, the distributed job scheduler 308 may schedule a virtual machine snapshot job to be run on a node in a cluster that is used to store snapshots of the virtual machine in order to reduce network congestion.

The distributed job scheduler 308 may comprise a distributed fault tolerant job scheduler, in which jobs affected by node failures are recovered and rescheduled to be run on available nodes. In one embodiment, the distributed job scheduler 308 may be fully decentralized and implemented without the existence of a master node. The distributed job scheduler 308 may run job scheduling processes on each node in a cluster or on a plurality of nodes in the cluster. In one example, the distributed job scheduler 308 may run a first set of job scheduling processes on a first node in the cluster, a second set of job scheduling processes on a second node in the cluster, and a third set of job scheduling processes on a third node in the cluster. The first set of job scheduling processes, the second set of job scheduling processes, and the third set of job scheduling processes may store information regarding jobs, schedules, and the states of jobs using a metadata store, such as distributed metadata store 310. In the event that the first node running the first set of job scheduling processes fails (e.g., due to a network failure or a physical machine failure), the states of the jobs managed by the first set of job scheduling processes may fail to be updated within a threshold period of time (e.g., a job may fail to be completed within 30 seconds or within minutes from being started). In response to detecting jobs that have failed to be updated within the threshold period of time, the distributed job scheduler 308 may undo and restart the failed jobs on available nodes within the cluster.

The job scheduling processes running on at least a plurality of nodes in a cluster (e.g., on each available node in the cluster) may manage the scheduling and execution of a plurality of jobs. The job scheduling processes may include run processes for running jobs, cleanup processes for cleaning up failed tasks, and rollback processes for rolling-back or undoing any actions or tasks performed by failed jobs. In one embodiment, the job scheduling processes may detect that a particular task for a particular job has failed and, in response, may perform a cleanup process to clean up or remove the effects of the particular task and then perform a rollback process that processes one or more completed tasks for the particular job in reverse order to undo the effects of the one or more completed tasks. Once the particular job with the failed task has been undone, the job scheduling processes may restart the particular job on an available node in the cluster.

The distributed job scheduler 308 may manage a job in which a series of tasks associated with the job are to be performed atomically (i.e., partial execution of the series of tasks is not permitted). If the series of tasks cannot be completely executed or there is any failure that occurs to one of the series of tasks during execution (e.g., a hard disk associated with a physical machine fails or a network connection to the physical machine fails), then the state of a data management system may be returned to a state as if none of the series of tasks was ever performed. The series of tasks may correspond with an ordering of tasks for the series of tasks and the distributed job scheduler 308 may ensure that each task of the series of tasks is executed based on the ordering of tasks. Tasks that do not have dependencies with each other may be executed in parallel.

In some cases, the distributed job scheduler 308 may schedule each task of a series of tasks to be performed on a specific node in a cluster. In other cases, the distributed job scheduler 308 may schedule a first task of the series of tasks to be performed on a first node in a cluster and a second task of the series of tasks to be performed on a second node in the cluster. In these cases, the first task may have to operate on a first set of data (e.g., a first file stored in a file system) stored on the first node and the second task may have to operate on a second set of data (e.g., metadata related to the first file that is stored in a database) stored on the second node. In some embodiments, one or more tasks associated with a job may have an affinity to a specific node in a cluster.

In one example, if the one or more tasks require access to a database that has been replicated on three nodes in a cluster, then the one or more tasks may be executed on one of the three nodes. In another example, if the one or more tasks require access to multiple chunks of data associated with a virtual disk that has been replicated over four nodes in a cluster, then the one or more tasks may be executed on one of the four nodes. Thus, the distributed job scheduler 308 may assign one or more tasks associated with a job to be executed on a particular node in a cluster based on the location of data that may be required to be accessed by the one or more tasks.

In one embodiment, the distributed job scheduler 308 may manage a first job associated with capturing and storing a snapshot of a virtual machine periodically (e.g., every 30 minutes). The first job may include one or more tasks, such as communicating with a virtualized infrastructure manager, such as the virtualized infrastructure manager 222 in FIG. 2 , to create a frozen copy of the virtual machine and to transfer one or more chunks (or one or more files) associated with the frozen copy to a storage appliance, such as storage appliance 300 in FIG. 1 . The one or more tasks may also include generating metadata for the one or more chunks, storing the metadata using the distributed metadata store 310, storing the one or more chunks within the distributed file system 312, and communicating with the virtualized infrastructure manager 222 that the frozen copy of the virtual machine may be unfrozen or released from a frozen state. The metadata for a first chunk of the one or more chunks may include information specifying a version of the virtual machine associated with the frozen copy, a time associated with the version (e.g., the snapshot of the virtual machine was taken at 5:30 p.m. on Jun. 29, 2018), and a file path to where the first chunk is stored within the distributed file system 312 (e.g., the first chunk is located at /snapshotsNM_B/s1/s1.chunk1). The one or more tasks may also include deduplication, compression (e.g., using a lossless data compression algorithm such as LZ4 or LZ77), decompression, encryption (e.g., using a symmetric key algorithm such as Triple DES or AES-256), and decryption related tasks.

The virtualization interface 304 may provide an interface for communicating with a virtualized infrastructure manager managing a virtualization infrastructure, such as virtualized infrastructure manager 222 in FIG. 2 , and requesting data associated with virtual machine snapshots from the virtualization infrastructure. The virtualization interface 304 may communicate with the virtualized infrastructure manager using an Application Programming Interface (API) for accessing the virtualized infrastructure manager (e.g., to communicate a request for a snapshot of a virtual machine). In this case, storage appliance 300 may request and receive data from a virtualized infrastructure without requiring agent software to be installed or running on virtual machines within the virtualized infrastructure. The virtualization interface 304 may request data associated with virtual blocks stored on a virtual disk of the virtual machine that have changed since a last snapshot of the virtual machine was taken or since a specified prior point in time. Therefore, in some cases, if a snapshot of a virtual machine is the first snapshot taken of the virtual machine, then a full image of the virtual machine may be transferred to the storage appliance. However, if the snapshot of the virtual machine is not the first snapshot taken of the virtual machine, then only the data blocks of the virtual machine that have changed since a prior snapshot was taken may be transferred to the storage appliance.

The virtual machine search index 306 may include a list of files that have been stored using a virtual machine and a version history for each of the files in the list. Each version of a file may be mapped to the earliest point-in-time snapshot of the virtual machine that includes the version of the file or to a snapshot of the virtual machine that includes the version of the file (e.g., the latest point in time snapshot of the virtual machine that includes the version of the file). In one example, the virtual machine search index 306 may be used to identify a version of the virtual machine that includes a particular version of a file (e.g., a particular version of a database, a spreadsheet, or a word processing document). In some cases, each of the virtual machines that are backed up or protected using storage appliance 300 may have a corresponding virtual machine search index.

In one embodiment, as each snapshot of a virtual machine is ingested, each virtual disk associated with the virtual machine is parsed in order to identify a file system type associated with the virtual disk and to extract metadata (e.g., file system metadata) for each file stored on the virtual disk. The metadata may include information for locating and retrieving each file from the virtual disk. The metadata may also include a name of a file, the size of the file, the last time at which the file was modified, and a content checksum for the file. Each file that has been added, deleted, or modified since a previous snapshot was captured may be determined using the metadata (e.g., by comparing the time at which a file was last modified with a time associated with the previous snapshot). Thus, for every file that has existed within any of the snapshots of the virtual machine, a virtual machine search index may be used to identify when the file was first created (e.g., corresponding with a first version of the file) and at what times the file was modified (e.g., corresponding with subsequent versions of the file). Each version of the file may be mapped to a particular version of the virtual machine that stores that version of the file.

In some cases, if a virtual machine includes a plurality of virtual disks, then a virtual machine search index may be generated for each virtual disk of the plurality of virtual disks. For example, a first virtual machine search index may catalog and map files located on a first virtual disk of the plurality of virtual disks and a second virtual machine search index may catalog and map files located on a second virtual disk of the plurality of virtual disks. In this case, a global file catalog or a global virtual machine search index for the virtual machine may include the first virtual machine search index and the second virtual machine search index. A global file catalog may be stored for each virtual machine backed up by a storage appliance within a file system, such as distributed file system 312 in FIG. 3 .

The data management system 302 may comprise an application running on the storage appliance 300 that manages and stores one or more snapshots of a virtual machine. In one example, the data management system 302 may comprise a highest-level layer in an integrated software stack running on the storage appliance. The integrated software stack may include the data management system 302, the virtualization interface 304, the distributed job scheduler 308, the distributed metadata store 310, and the distributed file system 312.

In some cases, the integrated software stack may run on other computing devices, such as a server or computing device 108 in FIG. 1 . The data management system 302 may use the virtualization interface 304, the distributed job scheduler 308, the distributed metadata store 310, and the distributed file system 312 to manage and store one or more snapshots of a virtual machine. Each snapshot of the virtual machine may correspond with a point-in-time version of the virtual machine. The data management system 302 may generate and manage a list of versions for the virtual machine. Each version of the virtual machine may map to or reference one or more chunks and/or one or more files stored within the distributed file system 312. Combined together, the one or more chunks and/or the one or more files stored within the distributed file system 312 may comprise a full image of the version of the virtual machine.

FIG. 4 shows an example cluster 400 of a distributed decentralized database, according to some example embodiments. As illustrated, the example cluster 400 includes five nodes, nodes 1-5. In some example embodiments, each of the five nodes runs from different machines, such as physical machine 314 in FIG. 3 or virtual machine 220 in FIG. 2 . The nodes in the example cluster 400 can include instances of peer nodes of a distributed database (e.g., cluster-based database, distributed decentralized database management system, a NoSQL database, Apache Cassandra, DataStax, MongoDB, CouchDB), according to some example embodiments. The distributed database system is distributed in data is sharded or distributed across the example cluster 400 in shards or chunks and decentralized in that there is no central storage device and no single point of failure. The system operates under an assumption that multiple nodes may go down, up, or become non-responsive, and so on. Sharding is splitting up of the data horizontally and managing each shard separately on different nodes. For example, if the data managed by the example cluster 400 can be indexed using the 26 letters of the alphabet, node 1 can manage a first shard that handles records that start with A through E, node 2 can manage a second shard that handles records that start with F through J, and so on.

In some example embodiments, data written to one of the nodes is replicated to one or more other nodes per a replication protocol of the example cluster 400. For example, data written to node 1 can be replicated to nodes 2 and 3. If node 1 prematurely terminates, node 2 and/or 3 can be used to provide the replicated data. In some example embodiments, each node of example cluster 400 frequently exchanges state information about itself and other nodes across the example cluster 400 using gossip protocol. Gossip protocol is a peer-to-peer communication protocol in which each node randomly shares (e.g., communicates, requests, transmits) location and state information about the other nodes in a given cluster.

Writing: For a given node, a sequentially written commit log captures the write activity to ensure data durability. The data is then written to an in-memory structure (e.g., a memtable, write-back cache). Each time the in-memory structure is full, the data is written to disk in a Sorted String Table data file. In some example embodiments, writes are automatically partitioned and replicated throughout the example cluster 400.

Reading: Any node of example cluster 400 can receive a read request (e.g., query) from an external client. If the node that receives the read request manages the data requested, the node provides the requested data. If the node does not manage the data, the node determines which node manages the requested data. The node that received the read request then acts as a proxy between the requesting entity and the node that manages the data (e.g., the node that manages the data sends the data to the proxy node, which then provides the data to an external entity that generated the request).

The distributed decentralized database system is decentralized in that there is no single point of failure due to the nodes being symmetrical and seamlessly replaceable. For example, whereas conventional distributed data implementations have nodes with different functions (e.g., master/slave nodes, asymmetrical database nodes, federated databases), the nodes of example cluster 400 are configured to function the same way (e.g., as symmetrical peer database nodes that communicate via gossip protocol, such as Cassandra nodes) with no single point of failure. If one of the nodes in example cluster 400 terminates prematurely (“goes down”), another node can rapidly take the place of the terminated node without disrupting service. The example cluster 400 can be a container for a keyspace, which is a container for data in the distributed decentralized database system (e.g., whereas a database is a container for containers in conventional relational databases, the Cassandra keyspace is a container for a Cassandra database system).

As mentioned above, conventional failover techniques can present a number of issues. The following example describes how these are addressed by some examples of the present disclosure. Assume that in a step 1, an administrator creates a blueprint (b1) with a VM (v1) in a cluster (C1) with a given virtual center (vcenter) configuration. The administrator designates a failover target to be cluster C2 with another vcenter configuration. In step 2, as the failover target is defined, the blueprint b1 is replicated to C2. In step 3, assuming a disaster (failure) happens in C1, the administrator performs a failover operation, and a new blueprint b2 with VM v2 in C2 is created. In step 4, at this point, the first snapshot of v2 is full in C2 as there is no snapshot history of v2 in C2. This situation can present a problem and some examples of the present disclosure therefore seek to establish a situation in which the first snapshot of v2 is incremental on the top of replicated snapshots of v1 (as v1 and v2 essentially represent the same entity).

In a step 5, assume that the administrator is able to bring up the cluster C1 and designates the failback target of b2 to be C1. In step 6, b2 starts replicating to cluster C1 as C1 has been designated as the failback target. In step 7, when replicating v2's snapshot from C2 to C1, a full replication is typically required as there is no snapshot of v2 in C1. This situation can present a further problem. Some examples of the present disclosure thus also seek to establish a situation in which the replicated snapshot is incremental on the top of the backup snapshot of v1 in C1 (snapshots of v1 taken before failover).

The above problems described in relation to steps 4 and 7 can have a significant impact on an administrator's data access and business continuity as taking a full backup and replication for a large number of VMs can be time consuming. These problems can become more complex to solve in certain circumstances, such as when an administrator seeks to perform multiple failovers and failbacks. In this situation, the administrator's production workload may be running in different datacenters in this order: primary datacenter->secondary datacenter->primary datacenter->secondary datacenter and so on. In such cases, new virtual machines may continually be created and all of them should be able to take incremental backup and incremental replication if needed.

In some further instances, an administrator can seek to failback to some tertiary datacenter (if the primary or secondary is not available). This production workload may be present in the order: primary datacenter->secondary datacenter->tertiary datacenter. Likewise, examples of the present disclosure also seek to address the same problem for this configuration.

In a further problematic scenario, an administrator may seek to perform a local recovery (for example, an instant recovery) in which a new virtual machine is created. In this example, after step 4, assume the administrator tries to perform an instant recovery of v2. This creates a new VM v3. Here again, examples of the present disclosure seek to establish situation in which the first snapshot of v3 is incremental on top of snapshots of v2.

Some examples to address these issues may include local data object linking. Here, a data object group is created which includes multiple data objects. Typically, all of these data objects represent the same virtual machine entity. This information is usually saved in a database and links to the data objects are managed and tracked during operations such as an instant recovery or a vCenter refresh.

In some examples, in one data object group there is only one active member. Data intensive backup or replication jobs are only scheduled to active members. These types of operations cannot, in some examples, be scheduled to members other than the active members. For this reason, only active members of the groups may be presented in cluster management user interfaces, for example. When querying APIs, inactive members are typically filtered out.

In some examples of the present disclosure, a VM (v1) in vCenter1 is protected by a cluster. Under that protection, various backup operations may occur from time to time. An example snapshot chain 502 of v1 used in such backup operations may be represented as shown in FIG. 5 . Assume an administrator triggers an instant recovery of v1 from snapshot S4. This operation creates a new VM v2 in the vcenter and it deprecates VM v1 accordingly. The VMs v1 and v2 form a data object group. At this point, v1 and v2 become a single entity. A rest API call to retrieve the snapshots of v2 retrieves all of the snapshots (S1, S2, S3, S4) in the snapshot chain 502. If the administrator schedules a backup of v2, the first backup will be on the top of S4.

In some examples, remote data object linking includes a grouping of remote data objects, as opposed to local data objects. The remote data object linking techniques can be applied to various types of clusters and data objects, for example native clusters, native data objects, and so-called continuous data management (CDM-native) data objects in the same data object group.

As mentioned above, present examples seek to enable incremental backup in relation to problematic steps 4 and 7 of the example scenario above. Some examples utilize remote data object linking in a failover job. In a rollforward task of a failover job, a data object group is created in C2 with VM v1 and VM v2, with v2 being an overall active member, for example as described above. Note that v1 belongs to cluster C1 and VM v2 belongs to cluster C2. With reference to step 4 above, when a new backup of v2 is taken, it is stored incrementally on the top of the replicated snapshots of v1 (as v2's list of snapshots will return all of v1's replicated snapshots). Again with reference to FIG. 5 , assume now that snapshots S1-S4 are the replicated snapshots of v1 on cluster C2. After failover, the first backup of v2 will be on the top of S4.

Linking addresses a problem of storing incremental snapshots in a cluster, but in some examples data ingestion from the vCenter to the cluster may still be full. One reason for this may be inconsistent changed block tracking (CBT) information between snapshot S4 and the new snapshot of v2. The CBT data for snapshot S4 will correspond to the vCenter attached to cluster C1 whereas the new backup of v2 occurs in the vCenter connected to cluster C2. Here, some examples update the CBT information for S4 to be compatible with the new vCenter in C2, which also enables incremental data ingestion.

Turning now to the issues of step 7 of the above scenario, a remote linking approach is now described in the context of a source cluster refresh (SCR) job. Here, in a step 6, before replication starts, cluster C1 initially schedules a SCR job, which refreshes or copies all the metadata from cluster C2 to C1. In this step, since v1 and v2 both belong to a data object group in cluster C2 (created during the failover job), some examples can also create a data object group of v1 and v2 in cluster C1. Once that is completed, the replication job identifies that snapshots of v2 already exist in C1 and the replication is therefore incremental.

Creating and managing the data object groups in an SCR operation can be difficult. As multiple clusters are involved, inconsistencies can exist between different clusters, which require appropriate handling. One example herein may include a blueprint b1 with VM v1 in the cluster C1. Assume this example failed to cluster C2 and instead created a new blueprint b2 with VM v2. The example now includes (v1, v2) data object groups in C2 with v2 being the overall active member. In one scenario, cluster C1 is unreachable when the failover is performed due to a disaster, for example. But assume, after some time, C1 comes back up again. Nevertheless, as the cluster C1 was unreachable during the failover, v1 was not deprecated in C1.

Therefore, if the administrator seeks to perform an instant recovery of v1 in C1 a new VM v3 is created, and this creates a data object group (v1, v3) with v3 being the active member. If the user adds C1 as the failback target for the blueprint b2, the SCR job will ideally try to link v1 and v2 in C1 but it will then see that v1 is already a part of a data object group with v3 being an active member. Some examples herein thus seek to resolve this discrepancy and determine whether v2 or v3 should be overall active members. An example algorithm for remote data object linking in an SCR job is provided further below.

Example use cases for remote data object linking may include the following. A first example use case relates to a vCenter refresh; here, examples link the newly discovered virtual machines during a vCenter refresh. An example heuristic is that the newly discovered VMs should have either the same instanceUuid or the same uuid and same name as the existing VMs. A second example use case includes an instant recovery. A third example use case includes a failover in AppFlows. Here, some examples link remote and local data objects during a failover. In some examples, the remote data object is a native data object. Other examples enable remote and local data object linking during a failover. Both the local and remote data objects may be CDM-native data objects. A fourth use case example may include a linking tool in which remote data object linking occurs using a data object name, for example to enforce instanceUuid and a uuid check. Here, some examples only link local data objects, while other examples include a tool to link local and remote data objects.

Some examples of remote data object linking may include certain rules or restrictions to avoid invalid states arising. For example, one data object group can only contain data objects of its own cluster and a maximum of one different cluster (the other cluster can be a native source, for example which may be considered as one cluster here). If any data object group in a CDM cluster (C1) contains the native data object of some native source (P 1), that data object group cannot contain a data object of any other CDM cluster or any other native source. During SCR, when a target pulls data object information from the source, the source will only send any data object group related information for data objects that either belong to the source or the target.

Other restrictions may relate to trust levels or verification. For example, in a combined local and remote linking of data objects, a local cluster may be more trusted than a remote cluster. For instance, assume an example includes two different clusters, cluster s (source) and cluster t (target). The example may include two different data objects, s1 and s2, that are associated with the source and two different data objects, t1 and t2, that are associated with the target. During SCR, the source may indicate that the data objects t1 and t2 are linked together. But t1 and t2 are not in the same data object group in the target. In this case, because t1 and t2 are local to the target cluster, the example will trust the information from the target cluster and will not link both of them together. In a further restriction, in some examples, when a source cluster replicates to a target cluster, the source cluster only replicates snapshots which belong to or are associated with its local data objects.

Some examples include a Data objectGroup schema to accommodate the linking of local and remote data objects. In this regard, a schema may include specific columns in a data object group table. As discussed above, each data object group can contain the data objects for two different clusters. Some examples include different subgroups for different clusters within a data object group. For example, a data object group comprising local and remote data objects will look like these (S and T are two different cluster): {{S: (s1,1), (s2,2)}, {T: (t1,1), (t2,2)}}. An example approach to identify the overall active member is discussed further below.

There may be a number of reasons for creating subgroups within a data object group. One reason may include a desire to identify the next active member while unlinking an active member from the data object group. The next active member will belong to the same clusterId. For example, assume there are two different clusters S and T (source and target) and the active member corresponds to S (the source cluster). If we unlink an active member of S from the data object group, we should not mark any member of T as active. This can create a different set of problems where we may start replication (if S is remote) or backup process (if S is local), which may not be intended. In the above example, if there are no more data objects belonging to S after unlinking, activeMember will be marked “None.”

Another reason for subgrouping may arise during a metadata replication using an SCR job. During SCR, it is possible that an update of an existing data object group is desired by adding some extra data objects in the data object group without changing its active members. In this case, if there is no subgrouping, then it might be the case that some other member is designated as the overall active member, which is not intended. For example, assume we are replicating from source S to target T. There is a data object group sg1 in the target: {S: {(s1:1), (s2:2)}, T: {(t1,1)}} where t1 is the active member. Now, S communicates to T that s1, s2, and s3 are linked together. Now the sg1 in the target will be updated to {S: {(s1:1), (s2:2), (s3:3)}, T: {(t1,1)}} with still t1 being the active member. If there was no subgrouping, s3 would have the highest sequence number and would have become an active member.

In some examples, a data object group includes members and member identifications (IDs). An example listing of data object members and member IDs, with some explanatory comments, is provided in FIG. 6 .

Example operations in an example algorithm 710 for SCR are now described with reference to FIGS. 7-9 .

Example operation 710 includes:

-   -   1. A target cluster T asks a source cluster S to send data         object information for a data object s1. The source sends the         following information about the data object group of s1:         -   a. clusterIdToMemberSequenceMap             -   i. Here the source S will send all the members of its                 own cluster and the target cluster T if any. It is                 possible that clusterIdToMemberSequenceMap has member of                 its own (source) cluster and some other cluster let's                 say C. In this case, S will only send the data object                 information of its own cluster.         -   b. memberTypeMap: only for those members which are sent             above.         -   c. forbiddenMember: only belonging to either S or T         -   d. jsonSpec         -   e. overallActiveMemberClusterId: If it is either belongs to             S or T. If not, send None         -   f. overallActiveMember: If it either belongs to S or T. If             not, send None         -   g. directlyLinkedRemoteMembers between S and T

Example operation 712 includes:

-   -   2. After receiving the linking information from the source, the         target performs either one of the below two operations         -   a. Update an existing data object group             -   i. Just to revisit the assumption added before, we will                 trust the target cluster for the linking information of                 data objects that belongs to the target cluster.             -   ii. There can be a potential problem while removing any                 source data object from a data object group in the                 target. If any source data objects are removed, then it                 may become active for some time period and it can start                 some job, which is not intended. For example: let us say                 we have this data object group sg1 in the source cluster                 S: {s1,s2} and sg2 in the target cluster T: {s1, s2, t1}                 with s2−t1 as the remote linking which happened during                 Failover. Now, in the source cluster, the data object                 group was updated to {s1, s3} and s2 (s2 was removed and                 then s3 was added). Now, if s2 will replicate to T, it                 can create s1 and {s2, t1}, which will make s1 as an                 active data object, which is not the case in S.             -   iii. To accommodate the above problem, we will not                 remove s1 from the data object group sg2. Instead, we                 will create a new data object group with the remaining                 members. For example, in the above case, after s2 is                 replicated to T, the data object group in T will look                 something like sg2: {s1}, sg3: {s2,t1}. The                 overallActiveMember of sg2 will be None (it was t1                 initially and was be changed to None when t1 was                 removed) and activeMemberClusterId will still be T. sg2                 was present initially and sg3 is the newly created data                 object group. The main motivation of doing this is to                 avoid making s1 an active member in cluster T until it                 receives the metadata information of s1 during SCR job.             -   iv. In the above kinds of cases, we need to give special                 attention to the forbiddenMembers. All the previous                 forbiddenMembers of sg2+current data objects present in                 sg2 will be copied to sg3's forbiddenMembers. sg2                 forbiddenMembers will be empty as it only contains the                 data objects that should not have been part of any data                 object group. Here the intent of creating a new data                 object group was to avoid making any data object as an                 active member. This type of creation of a new data                 object group will be different than what we will discuss                 in point b below (wrt forbiddenMembers) where the intent                 of creating a new data object group is inconsistency                 among the existing data object group.             -   v. There is one more catch here. Let us say we figure                 out some set of source data objects that is going to be                 added to some data object group sg1 in the target. If                 any of those source data objects are already present in                 the forbiddenMembers of sg1, then we will create a new                 data object group (discussed in the point b below)                 instead of updating it.         -   b. Create a new data object group             -   i. In the heuristic defined below, we may have to create                 a new data object group if we run into any inconsistency                 in the existing data object group in the target.             -   ii. Due to this, we may remove a data object from an                 existing data object group. Whenever we remove a data                 object from the data object group, we will remove it                 from clusterIdToMemberSequenceMap, memberTypeMap,                 overallActiveMember, directlyLinkedRemoteMembers if any.                 We will also add it to the forbiddenMembers of that data                 object group.

Example operation 714 includes:

-   -   3. Whenever we remove a data object from data object group in         the target during SCR, we won't be changing         activeMemberClusterId unless specified below (in 3.c.iii.3.a         below). There can be two different cases here:         -   a. The active member of the data object group is the target:             -   i. We are anyway not updating any target data objects                 view from the data object group so it will always be an                 active member.         -   b. The active member of the data object is the source:             -   i. In all the normal flow, whenever we finish the SCR                 job, there should be some source member which will                 become active. We will change overallActiveMember to                 None while removing a data object from a data object                 group if it was the overallActiveMember. There should be                 some different source data object that will be                 replicated and should become active in this data object                 group. There are still two cases where there would be no                 active member:                 -   1. If the customer has done unlinking of remote data                     objects which were linked during Failover. In this                     case, it is possible that the data object group                     overallActiveMemberClusterId is source and there are                     no source data objects in the data object group. We                     will not change the activeMemberClusterId as this                     can lead to different operations which are not                     intended. We will provide options in the tool to                     update the overallActiveMember.                 -   2. If the source data objects were archived in the                     source cluster. In that case, this data object group                     will have source data object as active member which                     is archived. This will either be cleaned up by a                     background job or using the tool.

Operation 716 includes:

-   -   4. If a data object group becomes empty after removing any of         its data object during the process, we will delete those data         object groups.

With reference to FIGS. 8 and 9A-9B, some example techniques are now described for creating a data object group in a target cluster, for example when replicating from a source cluster S to a target cluster T. In this example, s_(i)'s refer to data objects belonging to cluster S and Cs refer to data objects belonging to cluster T. FIG. 8 includes a listing of operations for Examples 1-2, and FIGS. 9A-9B include a listing of operations for Example 3. In each example, a target cluster T fetches a data objectConfig for data object s1 from a source cluster S.

With reference to FIG. 10 , certain operations in a method of remote data object linking are provided. In a data management and storage (DMS) cluster comprising peer DMS nodes and a distributed data store implemented across the peer DMS nodes, an example method 1000 of remote linking of data objects for data transfer between a first node cluster and a second node cluster among the peer DMS nodes comprises: at operation 1002, identifying the first node cluster; at operation 1004, identifying the second node cluster; at operation 1006, creating a data object group including multiple remote data objects, each remote data object representing a same first virtual machine, each remote data object registrable on at least the first and second node clusters of the peer DMS nodes; at operation 1008, creating or identifying remote links to each of the remote data objects in the data object group; at operation 1010, designating a single member of the data object group as an overall active member of the group; at operation 1012, assigning, to the active remember only, a backup or replication job of the virtual machine; and, at operation 1014, based on the assigned job, backing up the virtual machine using the remote data object links.

In some examples, the backup or replication job is associated with a failover operation or an instant recovery of the first virtual machine; and the method 1000 further comprises creating, in the second cluster, a second data object group comprising a first remote link to a first remote data object associated with the first virtual machine, and a second remote link to a second remote data object relating to a second virtual machine; and designating the second virtual machine as the overall active member.

In some examples, the method 1000 further comprises: based on a backup or replication request relating to the second virtual machine, storing a backup of the second virtual machine incrementally on top of replicated snapshots of the first virtual machine.

In some examples, the first node cluster is a source cluster; and the backup or replication job is associated with a source cluster refresh (SCR), the method 1000 further comprising: identifying the second data object group in the second cluster created during the failover operation or instant recovery of the first virtual machine; recognizing a same data object group in the first node cluster; and based on the recognition, performing the SCR incrementally.

In some examples, the method 1000 further comprises resolving a discrepancy in the designation of the overall active member in response to an identification of a new member of the data object group.

In some examples, the data object group further comprises at least one local data object, and the method 1000 further comprises linking data objects together based on an elevated level of trust assigned to the at least one local data object.

FIG. 11 is a block diagram illustrating an example of a computer software architecture for data classification and information security that may be installed on a machine, according to some example embodiments. FIG. 11 is merely a non-limiting example of a software architecture 1102, and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture 1102 may be executing on hardware such as a machine 1300 of FIG. 13 that includes, among other things, processor 1246, memory 1248, and I/O components 1250. A representative hardware layer 1104 of FIG. 11 is illustrated and can represent, for example, the machine 1300 of FIG. 13 . The representative hardware layer 1104 of FIG. 11 comprises one or more processing units 1106 having associated executable instructions 1108. The executable instructions 1108 represent the executable instructions of the software architecture 1102, including implementation of the methods, modules, and so forth described herein. The representative hardware layer 1104 also includes memory or storage modules 1110, which also have the executable instructions 1108. The representative hardware layer 1104 may also comprise other hardware 1112, which represents any other hardware of the representative hardware layer 1104, such as the other hardware illustrated as part of the machine 1100.

In the example architecture of FIG. 11 , the software architecture 1102 may be conceptualized as a stack of layers, where each layer provides particular functionality. For example, the software architecture 1102 may include layers such as an operating system 1114, libraries 1118, frameworks/middleware 1116, applications 1120, and a presentation layer 1142. Operationally, the applications 1120 or other components within the layers may invoke API calls 1122 through the software stack and receive a response, returned values, and so forth (illustrated as messages 1124) in response to the API calls 1122. The layers illustrated are representative in nature, and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware 1116 layer, while others may provide such a layer. Other software architectures may include additional or different layers.

The operating system 1114 may manage hardware resources and provide common services. The operating system 1114 may include, for example, a kernel 1128, services 1126, and drivers 1130. The kernel 1128 may act as an abstraction layer between the hardware and the other software layers. For example, the kernel 1128 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services 1126 may provide other common services for the other software layers. The drivers 1130 may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 1130 may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration.

The libraries 1118 may provide a common infrastructure that may be utilized by the applications 1120 and/or other components and/or layers. The libraries 1118 typically provide functionality that allows other software modules to perform tasks in an easier fashion than by interfacing directly with the underlying operating system 1114 functionality (e.g., kernel 1128, services 1126, or drivers 1130). The libraries 1118 may include system libraries 1132 (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 1118 may include API libraries 1134 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries 1118 may also include a wide variety of other libraries 1136 to provide many other APIs to the applications 1120 and other software components/modules.

The frameworks 1116 (also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by the applications 1120 or other software components/modules. For example, the frameworks 1116 may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks 1116 may provide a broad spectrum of other APIs that may be utilized by the applications 1120 and/or other software components/modules, some of which may be specific to a particular operating system or platform.

The applications 1120 include built-in applications 1138 and/or third-party applications 1140. Examples of representative built-in applications 1138 may include, but are not limited to, a home application, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, or a game application.

The third-party applications 1140 may include any of the built-in applications 1138, as well as a broad assortment of other applications. In a specific example, the third-party applications 1140 (e.g., an application developed using the Android™ or iOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as iOS™, Android™, Windows® Phone, or other mobile operating systems. In this example, the third-party applications 1140 may invoke the API calls 1122 provided by the mobile operating system such as the operating system 1114 to facilitate functionality described herein.

The applications 1120 may utilize built-in operating system functions (e.g., kernel 1128, services 1126, or drivers 1130), libraries (e.g., system libraries 1132, API libraries 1134, and other libraries 1136), or frameworks/middleware 1116 to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as the presentation layer 1142. In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with the user.

Some software architectures utilize virtual machines. In the example of FIG. 11 , this is illustrated by a virtual machine 1146. A virtual machine creates a software environment where applications/modules can execute as if they were executing on a hardware machine e.g., the machine 1300 of FIG. 13 , for example). A virtual machine 1146 is hosted by a host operating system (e.g., operating system 1114) and typically, although not always, has a virtual machine monitor 1144, which manages the operation of the virtual machine 1146 as well as the interface with the host operating system (e.g., operating system 1114). A software architecture executes within the virtual machine 1146, such as an operating system 1148, libraries 1156, frameworks/middleware 1154, applications 1152, or a presentation layer 1150. These layers of software architecture executing within the virtual machine 1146 can be the same as corresponding layers previously described or may be different.

FIG. 12 is a block diagram 1200 illustrating an architecture of software 1202, which can be installed on any one or more of the devices described above. FIG. 12 is merely a non-limiting example of a software architecture, and it will be appreciated that many other architectures can be implemented to facilitate the functionality described herein. In various embodiments, the software 1202 is implemented by hardware such as a machine 1300 of FIG. 13 that includes processor(s) 1246, memory 1248, and input/output (I/O) components 1250. In this example architecture, the software 1202 can be conceptualized as a stack of layers where each layer may provide a particular functionality. For example, the software 1202 includes layers such as an operating system 1204, libraries 1208, frameworks 1206, and applications 1210. Operationally, the applications 1210 invoke API calls 1212 (application programming interface) through the software stack and receive messages 1214 in response to the API calls 1212, consistent with some embodiments.

In various implementations, the operating system 1204 manages hardware resources and provides common services. The operating system 1004 includes, for example, a kernel 1216, services 1220, and drivers 1218. The kernel 1216 acts as an abstraction layer between the hardware and the other software layers, consistent with some embodiments. For example, the kernel 1216 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 1220 can provide other common services for the other software layers. The drivers 1218 are responsible for controlling or interfacing with the underlying hardware, according to some embodiments. For instance, the drivers 1218 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., USB drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

In some embodiments, the libraries 1208 provide a low-level common infrastructure utilized by the applications 1210. The libraries 1208 can include system libraries 1222 (e.g., C standard library) that can provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 1208 can include API libraries 1224 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as MPEG4, H.264 or AVC, MP3, AAC, AMR audio codec, JPEG or JPG, or PNG), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 1208 can also include a wide variety of other libraries 1226 to provide many other APIs to the applications 1210.

The frameworks 1206 provide a high-level common infrastructure that can be utilized by the applications 1210, according to some embodiments. For example, the frameworks 1206 provide various GUI functions, high-level resource management, high-level location services, and so forth. The frameworks 1206 can provide a broad spectrum of other APIs that can be utilized by the applications 1210, some of which may be specific to a particular operating system or platform.

In an example embodiment, the applications 1210 include a home application 1228, a contacts application 1230, a browser application 1232, a book reader application 1234, a location application 1236, a media application 1238, a messaging application 1240, a game application 1242, and a broad assortment of other applications, such as a third-party application 1244. According to some embodiments, the applications 1210 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 1210, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 1244 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 1244 can invoke the API calls 1212 provided by the operating system 1204 to facilitate functionality described herein.

FIG. 13 illustrates a diagrammatic representation of a machine 1300 in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically, FIG. 13 shows a diagrammatic representation of the machine 1300 in the example form of a computer system, within which instructions 1306 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1300 to perform any one or more of the methodologies discussed herein may be executed. Additionally, or alternatively, the instructions 1306 may implement the operations of the methods summarized or described herein.

The instructions 1306 transform the general, non-programmed machine 1300 into a particular machine 1300 programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine 1300 operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1300 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 1300 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 1306, sequentially or otherwise, that specify actions to be taken by the machine 1300. Further, while only a single machine 1300 is illustrated, the term “machine” shall also be taken to include a collection of machines 1300 that individually or jointly execute the instructions 1306 to perform any one or more of the methodologies discussed herein.

The machine 1300 may include processor(s) 1246, memory 1248, and I/O components 1250, which may be configured to communicate with each other such as via a bus 1302. In an example embodiment, the processor(s) 1246 (e.g., a CPU, a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a GPU, a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1304 and a processor 1308 that may execute the instructions 1306. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 13 shows multiple processor(s) 1246, the machine 1300 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory 1248 may include a main memory 1312, a static memory 1310, and a storage unit 1316, each accessible to the processor(s) 1246 such as via the bus 1302. The main memory 1312, the static memory 1310, and storage unit 1316 store the instructions 1306 embodying any one or more of the methodologies or functions described herein. The instructions 1306 may also reside, completely or partially, within the main memory 1312, within the static memory 1310, within the storage unit 1316, within at least one of the processor(s) 1246 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1300.

The I/O components 1250 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1250 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1250 may include many other components that are not shown in FIG. 13 . The I/O components 1250 are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components 1250 may include output components 1320 and input components 1322. The output components 1320 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 1322 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

In further example embodiments, the I/O components 1250 may include biometric components 1324, motion components 1326, environmental components 1328, or position components 1330, among a wide array of other components. For example, the biometric components 1324 may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 1326 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 1328 may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 1330 may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components 1250 may include communication components 1334 operable to couple the machine 1300 to a network 1338 or devices 1332 via a coupling 1314 and a coupling 1336, respectively. For example, the communication components 1334 may include a network interface component or another suitable device to interface with the network 1338. In further examples, the communication components 1334 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 1332 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1334 may detect identifiers or include components operable to detect identifiers. For example, the communication components 1334 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 1334, such as location via IP geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

The various memories (i.e., memory 1248, main memory 1312, and/or static memory 1310) and/or storage unit 1316 may store one or more sets of instructions and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 1306), when executed by processor(s) 1246, cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. In some examples, a storage device or medium accepts or receives random writes The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1338 may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network 1338 or a portion of the network 1338 may include a wireless or cellular network, and the coupling 1314 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling 1314 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long range protocols, or other data transfer technology.

The instructions 1306 may be transmitted or received over the network 1338 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 1334) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 1306 may be transmitted or received using a transmission medium via the coupling 1336 (e.g., a peer-to-peer coupling) to the devices 1332. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 1306 for execution by the machine 1300, and includes digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. In some examples, the storage devices/media accept or receiving random writes.

Although examples have been described with reference to specific example embodiments or methods, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the embodiments. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This detailed description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description. 

What is claimed is:
 1. A method, comprising: creating, in a first node cluster, a first data object representing a data entity; creating, in a second node cluster and after creating the first data object, a second data object representing the data entity, wherein a data object group comprises a first reference to the first data object and a second reference to the second data object; designating, based at least in part on creating the second data object, the second data object as an overall active member of the data object group; and assigning, to the overall active member, a task for a failover operation, a task for a source cluster refresh operation, a task for an instant recovery operation associated with the data entity, or any combination thereof.
 2. The method of claim 1, further comprising: adding, based at least in part on creating the first data object, the first data object to the data object group; and designating the first data object as the overall active member of the data object group.
 3. The method of claim 2, further comprising: adding, after designating the first data object as the overall active member and based at least in part on creating the second data object, the second data object to the data object group.
 4. The method of claim 1, further comprising: generating, after designating the second data object as the overall active member, backup data for the second data object, wherein the backup data for the second data object is stored incrementally to previously stored backup data for the first data object.
 5. The method of claim 4, wherein generating the backup data for the second data object comprises: capturing a snapshot of the second data object that is incremental to a most recent snapshot of the first data object.
 6. The method of claim 4, further comprising: updating first changed block tracking information for a most recent snapshot of the first data object to be compatible with a second virtual machine center running on the second node cluster.
 7. The method of claim 6, wherein incremental data ingestion for a snapshot of the second data object on top of the most recent snapshot of the first data object is enabled based at least in part on updating the first changed block tracking information.
 8. The method of claim 1, further comprising: linking, based at least in part on creating the second data object, the first data object with the second data object during a virtual machine center refresh based at least in part on the first data object and the second data object having a same unique identifier, a same name, or both.
 9. The method of claim 1, further comprising: limiting the data object group to data objects of the first node cluster and data objects of the second node cluster.
 10. The method of claim 1, further comprising: ignoring, by the second node cluster, a link indicated by the first node cluster between the second data object of the second node cluster and a third data object of the second node cluster based at least in part on an absence of an existing link between the second data object and the third data object at the second node cluster.
 11. The method of claim 1, further comprising: replicating, by the second node cluster, the data object group to a third node cluster, wherein replicating the data object group comprises: limiting a replication of snapshots for the data object group to data objects of the second node cluster.
 12. The method of claim 1, further comprising: generating, within the data object group, a first subgroup comprising references to data objects of the first node cluster and a second subgroup comprising references to data objects of the second node cluster.
 13. The method of claim 12, further comprising: designating, after designating the second data object as the overall active member, a third data object of the first node cluster as the overall active member of the data object group; unlinking, after designating the third data object as the overall active member, the third data object from the data object group; and refraining, based at least in part on the third data object being located at the first node cluster, from designating the second data object of the second node cluster as the overall active member of the data object group.
 14. The method of claim 13, further comprising: determining, based at least in part on unlinking the third data object of the first node cluster, an absence of data objects in the first subgroup associated with the first node cluster; and indicating that the data object group lacks an overall active member based at least in part on the absence of data objects in the first subgroup.
 15. The method of claim 12, further comprising: adding, after designating the second data object as the overall active member, a third data object to the first subgroup associated with the first node cluster; and maintaining the second data object as the overall active member after adding the third data object to the first subgroup associated with the first node cluster.
 16. An apparatus, comprising: one or more processors; and one or more memories storing code comprising instructions executable by the one or more processors to cause the apparatus to: create, in a first node cluster, a first data object representing a data entity; create, in a second node cluster and after creating the first data object, a second data object representing the data entity, wherein a data object group comprises a first reference to the first data object and a second reference to the second data object; designate, based at least in part on creating the second data object, the second data object as an overall active member of the data object group; and assign, to the overall active member, a task for a failover operation, a task for a source cluster refresh operation, a task for an instant recovery operation associated with the data entity, or any combination thereof.
 17. The apparatus of claim 16, wherein the instructions are further executable by the one or more processors to cause the apparatus to: add, based at least in part on creating the first data object, the first data object to the data object group; and designate the first data object as the overall active member of the data object group.
 18. The apparatus of claim 16, wherein the instructions are further executable by the one or more processors to cause the apparatus to: generate, after designating the second data object as the overall active member, backup data for the second data object, wherein the backup data for the second data object is stored incrementally to previously stored backup data for the first data object.
 19. The apparatus of claim 16, wherein the instructions are further executable by the one or more processors to cause the apparatus to: limit the data object group to data objects of the first node cluster and data objects of the second node cluster.
 20. A non-transitory, computer-readable medium storing code executable by one or more processors of a device to cause the device to: create, in a first node cluster, a first data object representing a data entity; create, in a second node cluster and after creating the first data object, a second data object representing the data entity, wherein a data object group comprises a first reference to the first data object and a second reference to the second data object; designate, based at least in part on creating the second data object, the second data object as an overall active member of the data object group; and assign, to the overall active member, a task for a failover operation, a task for a source cluster refresh operation, a task for an instant recovery operation associated with the data entity, or any combination thereof. 